We present an investigation into crossover in
Grammatical Evolution that begins by examining a
biologically-inspired homologous crossover operator
that is compared to standard one and two-point
operators. Results demonstrate that this homologous
operator is no better than the simpler one-point
operator traditionally adopted. An analysis of the
effectiveness of one-point crossover is then conducted
by determining the effects of this operator, by
adopting a headless chicken-type crossover that swaps
randomly generated fragments in place of the evolved
strings. Experiments show detrimental effects with the
utility of the headless chicken operator. Finally, the
mechanism of crossover in GE is analysed and termed
ripple crossover, due to its defining characteristics.
An experiment is described where ripple crossover is
applied to tree-based genetic programming, and the
results show that ripple crossover is more effective in
exploring the search space of possible programs than
sub-tree crossover by examining the rate of premature
convergence during the run. Ripple crossover produces
populations whose fitness increases gradually over
time, slower than, but to an eventual higher level than
that of sub-tree crossover.
%0 Journal Article
%1 oneill:2003:GPEM
%A O'Neill, Michael
%A Ryan, Conor
%A Keijzer, Maarten
%A Cattolico, Mike
%D 2003
%J Genetic Programming and Evolvable Machines
%K algorithms, chicken crossover crossover, evolution, genetic grammatical headless homologous programming, ripple sub-tree
%N 1
%P 67--93
%R doi:10.1023/A:1021877127167
%T Crossover in Grammatical Evolution
%V 4
%X We present an investigation into crossover in
Grammatical Evolution that begins by examining a
biologically-inspired homologous crossover operator
that is compared to standard one and two-point
operators. Results demonstrate that this homologous
operator is no better than the simpler one-point
operator traditionally adopted. An analysis of the
effectiveness of one-point crossover is then conducted
by determining the effects of this operator, by
adopting a headless chicken-type crossover that swaps
randomly generated fragments in place of the evolved
strings. Experiments show detrimental effects with the
utility of the headless chicken operator. Finally, the
mechanism of crossover in GE is analysed and termed
ripple crossover, due to its defining characteristics.
An experiment is described where ripple crossover is
applied to tree-based genetic programming, and the
results show that ripple crossover is more effective in
exploring the search space of possible programs than
sub-tree crossover by examining the rate of premature
convergence during the run. Ripple crossover produces
populations whose fitness increases gradually over
time, slower than, but to an eventual higher level than
that of sub-tree crossover.
@article{oneill:2003:GPEM,
abstract = {We present an investigation into crossover in
Grammatical Evolution that begins by examining a
biologically-inspired homologous crossover operator
that is compared to standard one and two-point
operators. Results demonstrate that this homologous
operator is no better than the simpler one-point
operator traditionally adopted. An analysis of the
effectiveness of one-point crossover is then conducted
by determining the effects of this operator, by
adopting a headless chicken-type crossover that swaps
randomly generated fragments in place of the evolved
strings. Experiments show detrimental effects with the
utility of the headless chicken operator. Finally, the
mechanism of crossover in GE is analysed and termed
ripple crossover, due to its defining characteristics.
An experiment is described where ripple crossover is
applied to tree-based genetic programming, and the
results show that ripple crossover is more effective in
exploring the search space of possible programs than
sub-tree crossover by examining the rate of premature
convergence during the run. Ripple crossover produces
populations whose fitness increases gradually over
time, slower than, but to an eventual higher level than
that of sub-tree crossover.},
added-at = {2008-06-19T17:46:40.000+0200},
author = {O'Neill, Michael and Ryan, Conor and Keijzer, Maarten and Cattolico, Mike},
biburl = {https://www.bibsonomy.org/bibtex/2101e5eb6fcc2620c65cda6e83e0a0abc/brazovayeye},
doi = {doi:10.1023/A:1021877127167},
interhash = {78bbe34843d9a20515281791f35daf91},
intrahash = {101e5eb6fcc2620c65cda6e83e0a0abc},
issn = {1389-2576},
journal = {Genetic Programming and Evolvable Machines},
keywords = {algorithms, chicken crossover crossover, evolution, genetic grammatical headless homologous programming, ripple sub-tree},
month = {March},
notes = {Article ID: 5113073},
number = 1,
pages = {67--93},
timestamp = {2008-06-19T17:48:58.000+0200},
title = {Crossover in Grammatical Evolution},
volume = 4,
year = 2003
}